Regularizing Hard Examples in Adversarial Training is a technique that improves the robustness of neural networks by addressing the negative impact of hard-to-learn examples. The approach involves pruning hard examples from the training set, which has been shown to enhance generalization performance. In adversarial training, hard examples are fitted through memorization, which can degrade model robustness. The proposed method, difficulty proportional label smoothing (DPLS), adaptively mitigates the negative effect of hard examples, improving adversarial robustness. Theoretical and empirical analyses demonstrate the effectiveness of this approach, with experimental results indicating successful leverage of hard examples while circumventing their negative impact.
Difficulty proportional label smoothing
Neural networks
CIFAR-10, ImageNet
Adversarial robustness, generalization performance
Cloud-based, on-premises
Yes
Yes
Improved adversarial robustness, adaptive label smoothing
Yes
GPU for training
Linux, Windows, macOS
Compatible with existing deep learning frameworks
Enhanced model robustness against adversarial attacks
None
None
No
Limited community support
Research team from leading universities
Large-scale datasets
Low
Moderate
None
Ensuring ethical use of adversarial training techniques
Requires careful selection of hard examples
Cybersecurity, computer vision
Adversarial defense in image classification, secure AI systems
Tech companies, cybersecurity firms
Integrates with deep learning frameworks
Scalable to large datasets
Research team support
None
Command-line interface
Yes
English
Research grant funded
No
Academic collaborations
None
None
1.0
Research framework
No
Academic research
0.00
USD
Research license
01/01/2023
01/10/2023
+1-800-555-0199
Supports robust adversarial training in neural networks
Yes